Airport Runway FOD Detection from LFMCW Radar and Image Data

Airport runway Foreign Object Debris (FOD) jeopardizes flight safety and leads to a large amount of financial cost on flight maintenance constantly. Several FOD detection systems based on a variety of detection techniques and architectures have been developed. This paper gives a brief introduction of our FOD detection system, in comparison with FOD detection systems currently in the international market. What distinguish our system from all the others is that, our detection approach is based on both linear frequency modulated continuous wave (LFMCW) radar signal and image data. This innovation combines the advantages of radar in shape detection and image in appearance detection. As a result, it increases the FOD detection rate and reduces the false alarming rate. Experiments following Federal Aviation Administration (FAA) advisory indicate that our system has reached the FAA requirements.

[1]  Ch. Pichot,et al.  77 GHz FM-CW radar for FODs detection , 2010, The 7th European Radar Conference.

[2]  Tony Lindeberg,et al.  Scale Invariant Feature Transform , 2012, Scholarpedia.

[3]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[4]  Yu Xuelian,et al.  Airport Runway FOD Detection Based on LFMCW Radar Using Interpolated FFT and CLEAN , 2012, 2012 IEEE 12th International Conference on Computer and Information Technology.

[5]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[7]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[8]  Yu Xuelian,et al.  FOD Detection on Airport Runway with Clutter Map CFAR Plane Technique , 2012 .

[9]  Cordelia Schmid,et al.  Improving Bag-of-Features for Large Scale Image Search , 2010, International Journal of Computer Vision.

[10]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .